How to use a job title filter without shrinking your list to nothing
A job title filter works best when it matches the words a company actually uses, not the words in your head. Start with the category or industry, then add a short title, then narrow with seniority. If the count collapses, remove the least important filter rather than guessing new titles.
Titles in data are messier than titles in a job ad
Real records say Writer, Senior Writer, Content Lead, Head of Content and Founder depending on the company's size. A filter that only matches Writer misses three of those. Prefix matching on words helps, but it cannot know that Content Lead is the same job.
The practical answer is to search the vocabulary, not the concept. Type a fragment such as content, look at what exists, and pick from that list instead of inventing a phrase.
Order your filters by cost
Category first, because it is a real boundary and it is fast. Then title, because it narrows the most per character typed. Then seniority, location and country. Verification last, because it removes rows you may still want to see in a preview.
If the count drops to zero at step two, the title is wrong, not the category. Zero after step four usually means the geography is too specific.
What a good preview looks like
A useful preview tells you the count before you spend anything, and shows the shape of the matches: how many have an email, a phone, a LinkedIn profile. That shape is what you are really buying, not the headline number.
If the count is large but the email coverage is thin, the list is bigger than it is useful. Narrow until the coverage improves.
Seniority is not a title
Seniority is a normalised label; titles are what people call themselves. A Director of Operations might sit at seniority director in one dataset and manager in another, because the source recorded it differently.
Use the title to find the vocabulary, then use seniority only as a light second pass. If seniority removes the person you wanted, trust the title.
When a category has no titles that fit
Some categories have thin job-title data: small firms list Owner, General Manager or nothing. In those cases, filter by company and country and write to the most senior name you can find, rather than forcing a title that does not exist.
Read a sample of titles before you decide the category is wrong. The vocabulary is usually there, just not in the words you first tried.
When the title vocabulary is thin
Small companies often publish one generic contact and no titles. In that case the title filter is the wrong lever: filter by company category and geography, then read the names you find.
For categories with plenty of titles, the opposite is true: the title is the sharpest tool you have, and geography is the blunt one that quietly removes the right people.
The skill is knowing which situation you are in before you start typing.
What good looks like after a month
You can name the three titles that produced replies in your category, and you no longer guess at new ones: you type a fragment and read the list.
The filter stops being a source of frustration and becomes a five-second step at the start of a campaign.
Filter order and what to do when the count collapses
| Step | Filter | If the count drops |
|---|---|---|
| 1 | Category | Choose a broader category |
| 2 | Title fragment | Use a shorter, real fragment |
| 3 | Seniority | Drop it and read the titles |
| 4 | Country or city | Widen to country |
| 5 | Verification | Keep yes only if sending |
Counts shown before a reveal are free in the Scrape.Email workspace.
Before you narrow
- Category chosen
- Title typed as a fragment
- Suggestions inspected
- Seniority added only if needed
- Geography widened before titles changed
- Preview count and email coverage checked
A worked example, hypothetical
A team searching for marketing managers in one city gets 60 matches. Removing the city gives 900 but mixes three countries. Removing seniority first keeps 240 relevant rows and doubles email coverage.
The lesson is that the city was the expensive filter, not the title. Most teams change the title first because it is the field they typed.
- 1.Try the title alone
- 2.Add the country
- 3.Add seniority last
- 4.Read coverage, not just count
What this will not do
- Titles are self-reported and inconsistently named.
- A precise title can exclude the person who actually decides.
- Coverage varies by category and geography.
Questions people ask about this
Should I use seniority or a title?
Use a title first, then seniority as a second pass. Seniority is a normalised label and will not rescue a title that the data never uses.
Why does a title exist for one category but not another?
Each category is its own dataset, collected from different sources. The vocabulary differs, so check suggestions per category rather than assuming a title travels.
Can I search several titles at once?
Search one fragment at a time to keep the count meaningful, or run separate searches and merge the exports yourself.
Can a title filter reduce my email coverage?
It can, if the titles you keep are held by people whose addresses are less well published. Check coverage in the preview rather than assuming a smaller list is a better one.
Why do two similar categories return different titles?
Each category is collected from its own sources. Vocabulary differs by industry, so check suggestions inside the category you intend to use.